MSA, Multiple Sequence Alignment, PTM, Post Translational Modification, SSPF, Scottish Structural Proteomics Facility, MCC, Matthew’s correlation coefficient, AROC, Area Under the Receiver Operator Characteristic curve, Target selection, Crystallisation, Structural genomics, Structural biology, Bioinformatics, Construct design
► Identifies key considerations in target selection and optimisation. ► Approaches to assign useful protein features and structure/function relationships. ► Comparison of latest crystallisation propensity predictors on nonredundant data. ► Discusses single point of reference target selection/optimisation resources. ► Guidance on using the SSPF Target Optimisation Utility (TarO).
Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.
Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and TarO (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals.